Mapping targets to single cells in plaques.
Accompanying ‘MR_CVD_MDD’
Sander W. van der Laan, PhD | s.w.vanderlaan[at]gmail[dot]com
2024-10-31
General Setup
Create a new analysis directories.
- general directory
- for plots
- for output of summary results
- for baseline tables
- for genetic analyses
- for Cox regression resultsUpdate all/some/none? [a/s/n]: ERA-CVD ‘druggable-MI-targets’
For the ERA-CVD ‘druggable-MI-targets’ project (grantnumber: 01KL1802) we performed two related RNA sequencing (RNAseq) experiments:
conventional (‘bulk’) RNAseq using RNA extracted from carotid plaque samples, n ± 700. As of Thursday, October 31, 2024 all samples have been selected and RNA has been extracted; quality control (QC) was performed and we have a dataset of 635 samples.
single-cell RNAseq (scRNAseq) of at least n = 40 samples (20 females, 20 males). As of Thursday, October 31, 2024 data is available of 40 samples (3 females, 15 males), we are extending sampling to get more female samples.
Plaque samples are derived from carotid endarterectomies as part of the Athero-Express Biobank Study which is an ongoing study in the UMC Utrecht.
Background
Here we map the MR_CVD_MDD to single-cells from the plaques.
Targets
Here we obtain data from the MR_CVD_MDD in plaques.
library(openxlsx)
gene_list_df <- read.xlsx(paste0(PROJECT_loc, "/targets/targets.xlsx"), sheet = "Genes")
gene_list <- unlist(gene_list_df$Gene)
gene_list [1] "ADAP1" "BAHD1" "CHAF1A" "DISP2" "FAM98B" "GNA11" "GPR176" "KNL1" "MDFIC" "MPND" "NDUFA11" "PIP5K1C" "SUN1" "TLE5" "ZFAND2A"Load data
First we will load the data:
- scRNAseq experimental data and rename the cell types.
- Athero-Express clinical data.
Here we load the latest dataset from our Athero-Express single-cell RNA experiment.
# load(paste0(AESCRNA_loc, "/20210811.46.patients.KP.RData"))
# scRNAseqData <- seuset
# rm(seuset)
#
# saveRDS(scRNAseqData, paste0(AESCRNA_loc, "/20210811.46.patients.KP.RDS"))
scRNAseqData <- readRDS(paste0(AESCRNA_loc, "/20210811.46.patients.KP.RDS"))
scRNAseqDataAn object of class Seurat
36147 features across 4948 samples within 2 assays
Active assay: RNA (20111 features, 0 variable features)
3 layers present: counts, data, scale.data
1 other assay present: SCT
2 dimensional reductions calculated: pca, umapThe naming/classification is based on a combination conventional markers. We do not claim to know the exact identity of each cell, rather we refer to cells as ‘KIT+ Mast cells”-like cells. Likewise we refer to the cell clusters as ’communities’ of cells that exhibit similar properties, i.e. similar defining markers (e.g. KIT).
We will rename the cell types to human readable names.
### change names for clarity
backup.scRNAseqData = scRNAseqData
# get the old names to change to new names
UMAPPlot(scRNAseqData, label = FALSE, pt.size = 1.25, label.size = 4, group.by = "ident")
[1] CD3+ T Cells I CD3+ T Cells IV CD34+ Endothelial Cells I
[4] CD3+ T Cells V CD3+CD56+ NK Cells II CD3+ T Cells VI
[7] CD68+IL18+TLR4+TREM2+ Resident macrophages CD3+CD56+ NK Cells I ACTA2+ Smooth Muscle Cells
[10] CD3+ T Cells II FOXP3+ T Cells CD34+ Endothelial Cells II
[13] CD3+ T Cells III CD68+CD1C+ Dendritic Cells CD68+CASP1+IL1B+SELL+ Inflammatory macrophages
[16] CD79A+ Class-switched Memory B Cells CD68+ABCA1+OLR1+TREM2+ Foam Cells CD68+KIT+ Mast Cells
[19] CD68+CD4+ Monocytes CD79+ Plasma B Cells
20 Levels: CD3+ T Cells I CD3+ T Cells II CD3+ T Cells III CD3+ T Cells IV CD68+IL18+TLR4+TREM2+ Resident macrophages ... CD79+ Plasma B Cellscelltypes <- c("CD68+CD4+ Monocytes" = "CD68+CD4+ Mono",
"CD68+IL18+TLR4+TREM2+ Resident macrophages" = "CD68+IL18+TLR4+TREM2+ MRes",
"CD68+CD1C+ Dendritic Cells" = "CD68+CD1C+ DC",
"CD68+CASP1+IL1B+SELL+ Inflammatory macrophages" = "CD68+CASP1+IL1B+SELL MInf",
"CD68+ABCA1+OLR1+TREM2+ Foam Cells" = "CD68+ABCA1+OLR1+TREM2+ FC",
# T-cells
"CD3+ T Cells I" = "CD3+ TC I",
"CD3+ T Cells II" = "CD3+ TC II",
"CD3+ T Cells III" = "CD3+ TC III",
"CD3+ T Cells IV" = "CD3+ TC IV",
"CD3+ T Cells V" = "CD3+ TC V",
"CD3+ T Cells VI" = "CD3+ TC VI",
"FOXP3+ T Cells" = "FOXP3+ TC",
# Endothelial cells
"CD34+ Endothelial Cells I" = "CD34+ EC I",
"CD34+ Endothelial Cells II" = "CD34+ EC II",
# SMC
"ACTA2+ Smooth Muscle Cells" = "ACTA2+ SMC",
# NK Cells
"CD3+CD56+ NK Cells I" = "CD3+CD56+ NK I",
"CD3+CD56+ NK Cells II" = "CD3+CD56+ NK II",
# Mast
"CD68+KIT+ Mast Cells" = "CD68+KIT+ MC",
"CD79A+ Class-switched Memory B Cells" = "CD79A+ BCmem",
"CD79+ Plasma B Cells" = "CD79+ BCplasma")
scRNAseqData <- Seurat::RenameIdents(object = scRNAseqData,
celltypes)UMAPPlot(scRNAseqData, label = TRUE, pt.size = 1.25, label.size = 4, group.by = "ident",
repel = TRUE)
Clinical data
Loading the Athero-Express clinical data.
AEDB.CEA <- readRDS(file = paste0(OUT_loc, "/", Today,".",PROJECTNAME,".AEDB.CEA.RDS"))
# AEDB.CEA <- readRDS(file = paste0(OUT_loc, "/20240531.",PROJECTNAME,".AEDB.CEA.RDS"))# Baseline table variables
basetable_vars = c("Hospital", "ORyear", "Artery_summary",
"Age", "Gender",
# "TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU",
"TC_final", "LDL_final", "HDL_final", "TG_final",
# "hsCRP_plasma",
"systolic", "diastoli", "GFR_MDRD", "BMI",
"KDOQI", "BMI_WHO",
"SmokerStatus", "AlcoholUse",
"DiabetesStatus",
"Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs",
"Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
"Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
"Symptoms.Update2G", "Symptoms.Update3G", "indexsymptoms_latest_4g",
"restenos", "stenose",
"CAD_history", "PAOD", "Peripheral.interv",
"EP_composite", "EP_composite_time", "EP_major", "EP_major_time",
"MAC_rankNorm", "SMC_rankNorm", "Macrophages.bin", "SMC.bin",
"Neutrophils_rankNorm", "MastCells_rankNorm",
"IPH.bin", "VesselDensity_rankNorm",
"Calc.bin", "Collagen.bin",
"Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype", "Plaque_Vulnerability_Index",
"PCSK9_plasma", "PCSK9_plasma_rankNorm")
basetable_bin = c("Gender", "Artery_summary",
"KDOQI", "BMI_WHO",
"SmokerStatus", "AlcoholUse",
"DiabetesStatus",
"Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs",
"Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
"Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
"Symptoms.Update2G", "Symptoms.Update3G", "indexsymptoms_latest_4g",
"restenos", "stenose",
"CAD_history", "PAOD", "Peripheral.interv",
"EP_major", "EP_composite", "Macrophages.bin", "SMC.bin",
"IPH.bin",
"Calc.bin", "Collagen.bin",
"Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype", "Plaque_Vulnerability_Index")
# basetable_bin
basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_conAESCRNA: baseline characteristics
Preparation
metadata <- scRNAseqData@meta.data %>% as_tibble() %>% separate(orig.ident, c("Patient", NA))
scRNAseqDataMeta <- metadata %>% distinct(Patient, .keep_all = TRUE)distinct: removed 4,902 rows (99%), 46 rows remainingscRNAseqDataMetaAE <- merge(scRNAseqDataMeta, AEDB.CEA, by.x = "Patient", by.y = "STUDY_NUMBER", sort = FALSE, all.x = TRUE)
dim(scRNAseqDataMetaAE)[1] 46 1242# Replace missing data
# Ref: https://cran.r-project.org/web/packages/naniar/vignettes/replace-with-na.html
require(naniar)
na_strings <- c("NA", "N A", "N / A", "N/A", "N/ A",
"Not Available", "Not available",
"missing",
"-999", "-99",
"No data available/missing", "No data available/Missing")
# Then you write ~.x %in% na_strings - which reads as “does this value occur in the list of NA strings”.
scRNAseqDataMetaAE %>%
replace_with_na_all(condition = ~.x %in% na_strings)cat("====================================================================================================")====================================================================================================SELECTION THE SHIZZLE- sanity checking PRIOR to selectionlibrary(data.table)
require(labelled)
ae.gender <- to_factor(scRNAseqDataMetaAE$Gender)
ae.hospital <- to_factor(scRNAseqDataMetaAE$Hospital)
table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"), useNA = "ifany") Hospital
Sex St. Antonius, Nieuwegein UMC Utrecht <NA>
female 0 19 0
male 0 26 0
<NA> 0 0 1ae.artery <- to_factor(scRNAseqDataMetaAE$Artery_summary)
table(ae.artery, ae.gender, dnn = c("Sex", "Artery"), useNA = "ifany") Artery
Sex female male <NA>
No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA 0 0 0
carotid (left & right) 19 25 0
femoral/iliac (left, right or both sides) 0 0 0
other carotid arteries (common, external) 0 1 0
carotid bypass and injury (left, right or both sides) 0 0 0
aneurysmata (carotid & femoral) 0 0 0
aorta 0 0 0
other arteries (renal, popliteal, vertebral) 0 0 0
femoral bypass, angioseal and injury (left, right or both sides) 0 0 0
<NA> 0 0 1 ae.gender
ae.ic female male <NA>
missing 0 0 0
no, died 0 0 0
yes 9 14 0
yes, health treatment when possible 5 7 0
yes, no health treatment 2 2 0
yes, no health treatment, no commercial business 1 2 0
yes, no tissue, no commerical business 0 0 0
yes, no tissue, no questionnaires, no medical info, no commercial business 0 0 0
yes, no questionnaires, no health treatment, no commercial business 0 0 0
yes, no questionnaires, health treatment when possible 0 0 0
yes, no tissue, no questionnaires, no health treatment, no commerical business 0 0 0
yes, no health treatment, no medical info, no commercial business 0 0 0
yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business 0 0 0
yes, no questionnaires, no health treatment 0 0 0
yes, no tissue, no health treatment 0 0 0
yes, no tissue, no questionnaires 0 0 0
yes, no tissue, health treatment when possible 0 0 0
yes, no tissue 0 0 0
yes, no commerical business 1 1 0
yes, health treatment when possible, no commercial business 0 0 0
yes, no medical info, no commercial business 0 0 0
yes, no questionnaires 0 0 0
yes, no tissue, no questionnaires, no health treatment, no medical info 0 0 0
yes, no tissue, no questionnaires, no health treatment, no commercial business 0 0 0
yes, no medical info 0 0 0
yes, no questionnaires, no commercial business 0 0 0
yes, no questionnaires, no health treatment, no medical info 0 0 0
yes, no questionnaires, health treatment when possible, no commercial business 0 0 0
yes, no health treatment, no medical info 0 0 0
no, doesn't want to 0 0 0
no, unable to sign 0 0 0
no, no reaction 0 0 0
no, lost 0 0 0
no, too old 0 0 0
yes, no medical info, health treatment when possible 1 0 0
no (never asked for IC because there was no tissue) 0 0 0
yes, no medical info, no commercial business, health treatment when possible 0 0 0
no, endpoint 0 0 0
wil niets invullen, wel alles gebruiken 0 0 0
second informed concents: yes, no commercial business 0 0 0
nooit geincludeerd 0 0 0
yes, not outside EU 0 0 0
yes, no DNA 0 0 0
yes, no commercial business, not outside EU 0 0 0
yes, no DNA, no commercial business, not outside EU 0 0 0
<NA> 0 0 1rm(ae.gender, ae.hospital, ae.artery, ae.ic)
scRNAseqDataMetaAE.all <- subset(scRNAseqDataMetaAE,
(Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)" ) & # we only want carotids
informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
informedconsent != "no, died" &
informedconsent != "yes, no tissue, no commerical business" &
informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
informedconsent != "yes, no tissue, no health treatment" &
informedconsent != "yes, no tissue, no questionnaires" &
informedconsent != "yes, no tissue, health treatment when possible" &
informedconsent != "yes, no tissue" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
informedconsent != "no, doesn't want to" &
informedconsent != "no, unable to sign" &
informedconsent != "no, no reaction" &
informedconsent != "no, lost" &
informedconsent != "no, too old" &
informedconsent != "yes, no medical info, health treatment when possible" &
informedconsent != "no (never asked for IC because there was no tissue)" &
informedconsent != "no, endpoint" &
informedconsent != "nooit geincludeerd" &
informedconsent != "yes, no health treatment, no commercial business" & # IMPORTANT: since we are sharing with a commercial party
informedconsent != "yes, no tissue, no commerical business" &
informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
informedconsent != "yes, no questionnaires, no health treatment, no commercial business" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
informedconsent != "yes, no health treatment, no medical info, no commercial business" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
informedconsent != "yes, no commerical business" &
informedconsent != "yes, health treatment when possible, no commercial business" &
informedconsent != "yes, no medical info, no commercial business" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
informedconsent != "yes, no questionnaires, no commercial business" &
informedconsent != "yes, no questionnaires, health treatment when possible, no commercial business" &
informedconsent != "second informed concents: yes, no commercial business")
# scRNAseqDataMetaAE.all[1:10, 1:10]
dim(scRNAseqDataMetaAE.all)[1] 39 1242Baseline
Showing the baseline table for the scRNAseq data in 39 CEA patients with informed consent.
===========================================================================================CREATE BASELINE TABLE# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
scRNAseqDataMetaAE.all.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
# strata = "Gender",
data = scRNAseqDataMetaAE.all, includeNA = TRUE),
nonnormal = c(),
quote = FALSE, showAllLevels = TRUE,
format = "p",
contDigits = 3)[,1:2]Warning: These variables only have NA/NaN: Symptoms.Update2G Symptoms.Update3G indexsymptoms_latest_4g MAC_rankNorm SMC_rankNorm Neutrophils_rankNorm MastCells_rankNorm VesselDensity_rankNorm PCSK9_plasma PCSK9_plasma_rankNorm Dropped
level Overall
n 39
Hospital (%) St. Antonius, Nieuwegein 0.0
UMC Utrecht 100.0
ORyear (%) No data available/missing 0.0
2002 0.0
2003 0.0
2004 0.0
2005 0.0
2006 0.0
2007 0.0
2008 0.0
2009 0.0
2010 0.0
2011 0.0
2012 0.0
2013 0.0
2014 0.0
2015 0.0
2016 0.0
2017 0.0
2018 51.3
2019 35.9
2020 10.3
2021 2.6
2022 0.0
Artery_summary (%) No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA 0.0
carotid (left & right) 97.4
femoral/iliac (left, right or both sides) 0.0
other carotid arteries (common, external) 2.6
carotid bypass and injury (left, right or both sides) 0.0
aneurysmata (carotid & femoral) 0.0
aorta 0.0
other arteries (renal, popliteal, vertebral) 0.0
femoral bypass, angioseal and injury (left, right or both sides) 0.0
Age (mean (SD)) 72.077 (8.183)
Gender (%) female 41.0
male 59.0
TC_final (mean (SD)) 4.533 (1.252)
LDL_final (mean (SD)) 2.676 (1.013)
HDL_final (mean (SD)) 1.135 (0.229)
TG_final (mean (SD)) 1.927 (1.093)
systolic (mean (SD)) 150.842 (27.013)
diastoli (mean (SD)) 79.711 (16.432)
GFR_MDRD (mean (SD)) 79.036 (31.777)
BMI (mean (SD)) 26.332 (3.962)
KDOQI (%) No data available/missing 0.0
Normal kidney function 28.2
CKD 2 (Mild) 33.3
CKD 3 (Moderate) 28.2
CKD 4 (Severe) 0.0
CKD 5 (Failure) 0.0
<NA> 10.3
BMI_WHO (%) No data available/missing 0.0
Underweight 2.6
Normal 33.3
Overweight 38.5
Obese 17.9
<NA> 7.7
SmokerStatus (%) Current smoker 28.2
Ex-smoker 53.8
Never smoked 12.8
<NA> 5.1
AlcoholUse (%) No 38.5
Yes 53.8
<NA> 7.7
DiabetesStatus (%) Control (no Diabetes Dx/Med) 71.8
Diabetes 28.2
Hypertension.selfreport (%) No data available/missing 0.0
no 7.7
yes 87.2
<NA> 5.1
Hypertension.selfreportdrug (%) No data available/missing 0.0
no 7.7
yes 87.2
<NA> 5.1
Hypertension.composite (%) No data available/missing 0.0
no 7.7
yes 92.3
Hypertension.drugs (%) No data available/missing 0.0
no 10.3
yes 84.6
<NA> 5.1
Med.anticoagulants (%) No data available/missing 0.0
no 87.2
yes 5.1
<NA> 7.7
Med.all.antiplatelet (%) No data available/missing 0.0
no 20.5
yes 74.4
<NA> 5.1
Med.Statin.LLD (%) No data available/missing 0.0
no 20.5
yes 74.4
<NA> 5.1
Stroke_Dx (%) Missing 0.0
No stroke diagnosed 56.4
Stroke diagnosed 43.6
sympt (%) missing 0.0
Asymptomatic 15.4
TIA 17.9
minor stroke 25.6
Major stroke 10.3
Amaurosis fugax 15.4
Four vessel disease 0.0
Vertebrobasilary TIA 0.0
Retinal infarction 2.6
Symptomatic, but aspecific symtoms 2.6
Contralateral symptomatic occlusion 0.0
retinal infarction 2.6
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.0
retinal infarction + TIAs 0.0
Ocular ischemic syndrome 7.7
ischemisch glaucoom 0.0
subclavian steal syndrome 0.0
TGA 0.0
Symptoms.5G (%) Asymptomatic 15.4
Ocular 23.1
Other 2.6
Retinal infarction 5.1
Stroke 35.9
TIA 17.9
AsymptSympt (%) Asymptomatic 15.4
Ocular and others 30.8
Symptomatic 53.8
AsymptSympt2G (%) Asymptomatic 15.4
Symptomatic 84.6
restenos (%) missing 0.0
de novo 100.0
restenosis 0.0
stenose bij angioseal na PTCA 0.0
stenose (%) missing 0.0
0-49% 2.6
50-70% 10.3
70-90% 46.2
90-99% 25.6
100% (Occlusion) 0.0
NA 0.0
50-99% 0.0
70-99% 15.4
99 0.0
CAD_history (%) Missing 0.0
No history CAD 79.5
History CAD 20.5
PAOD (%) missing/no data 0.0
no 84.6
yes 15.4
Peripheral.interv (%) no 76.9
yes 23.1
EP_composite (%) No data available. 0.0
No composite endpoints 79.5
Composite endpoints 17.9
<NA> 2.6
EP_composite_time (mean (SD)) 2.459 (1.124)
EP_major (%) No data available. 0.0
No major events (endpoints) 87.2
Major events (endpoints) 10.3
<NA> 2.6
EP_major_time (mean (SD)) 2.588 (1.111)
Macrophages.bin (%) no/minor 20.5
moderate/heavy 64.1
<NA> 15.4
SMC.bin (%) no/minor 20.5
moderate/heavy 64.1
<NA> 15.4
IPH.bin (%) no 17.9
yes 66.7
<NA> 15.4
Calc.bin (%) no/minor 38.5
moderate/heavy 46.2
<NA> 15.4
Collagen.bin (%) no/minor 7.7
moderate/heavy 71.8
<NA> 20.5
Fat.bin_10 (%) <10% 25.6
>10% 59.0
<NA> 15.4
Fat.bin_40 (%) <40% 56.4
>40% 28.2
<NA> 15.4
OverallPlaquePhenotype (%) atheromatous 28.2
fibroatheromatous 30.8
fibrous 25.6
<NA> 15.4
Plaque_Vulnerability_Index (%) 0 15.4
1 20.5
2 15.4
3 33.3
4 10.3
5 5.1 Writing the baseline table to Excel format.
# Write basetable
require(openxlsx)
# write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AESCRNA.32pts.after_qc.IC_commercial.BaselineTable.xlsx"),
# format(as.data.frame(scRNAseqDataMetaAE.all.tableOne), digits = 5, scientific = FALSE),
# rowNames = TRUE, colNames = TRUE,
# sheetName = "AESCRNA", overwrite = TRUE)
write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AESCRNA.CEA.39pts.after_qc.IC_academic.BaselineTable.xlsx"),
format(as.data.frame(scRNAseqDataMetaAE.all.tableOne), digits = 5, scientific = FALSE),
rowNames = TRUE, colNames = TRUE,
sheetName = "AESCRNA_CEA", overwrite = TRUE)Subset scRNAseq data
List of samples to be included based on informed consent (see above).
[1] "4440" "4447" "4443" "4450" "4453" "4470" "4459" "4458" "4455" "4487" "4472" "4480" "4477" "4448" "4486" "4488" "4502" "4500" "4501" "4530" "4541" "4542"
[23] "4513" "4535" "4546" "4545" "4571" "4489" "4491" "4495" "4496" "4521" "4520" "4580" "4602" "4605" "4601" "4676" "4653"Just make sure to keep the rows
(patientid.plateid.cellid) in the
data, so we will create a new column for that.
Subsetting the samples of interest based on
samples_of_interest.
Selecting the clinical data of interest.
variables_of_interest <- c("Hospital", "ORyear", "Artery_summary",
"Age", "Gender",
"TC_final", "LDL_final", "HDL_final", "TG_final",
"systolic", "diastoli", "GFR_MDRD", "BMI",
"KDOQI", "BMI_WHO",
"SmokerStatus", "AlcoholUse",
"DiabetesStatus",
"Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs",
"Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
"Stroke_Dx",
"sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
"Symptoms.Update2G", "Symptoms.Update3G", "indexsymptoms_latest_4g",
"restenos", "stenose",
"CAD_history", "PAOD", "Peripheral.interv",
"EP_composite", "EP_composite_time", "EP_major", "EP_major_time")
temp <- subset(scRNAseqDataMetaAE.all, select = c("Patient", variables_of_interest))Now we are merging in the clinical data of interest.
# Step 1: Extract original metadata from Seurat object
original_metadata_df <- scRNAseqDataCEA39@meta.data
# Step 2: Merge with dataframe (temp) with select variables based on 'Patient' column
merged_metadata_df <- merge(original_metadata_df, temp, by = "Patient", all.x = TRUE, sort = FALSE)
# Step 3: Exclude already existing columns to avoid duplication
new_metadata_columns <- setdiff(colnames(temp), colnames(original_metadata_df))
new_meta_info_df <- merged_metadata_df[, new_metadata_columns, drop = FALSE]
# Step 4: Add the new metadata to the Seurat object
scRNAseqDataCEA39 <- AddMetaData(scRNAseqDataCEA39, metadata = new_meta_info_df)
# Step 5: Rename `Patient` to `STUDY_NUMBER` to match the clinical data
scRNAseqDataCEA39@meta.data <- dplyr::rename(scRNAseqDataCEA39@meta.data, "STUDY_NUMBER" = "Patient")
# Verify that the new metadata was added correctly
head(scRNAseqDataCEA39@meta.data)Saving new dataset
temp2 <- as_tibble(subset(scRNAseqDataCEA39@meta.data, select = c("STUDY_NUMBER", "orig.ident", "nCount_RNA", "nFeature_RNA",
"Plate", "Batch", "C.H", "Type", "percent.mt",
"nCount_SCT", "nFeature_SCT", "seurat_clusters")))
# fwrite(temp2,
# file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.samplelist.after_qc.IC_commercial.csv"),
# sep = ",", row.names = FALSE, col.names = TRUE,
# showProgress = TRUE)
# rm(temp2)
#
# temp <- dplyr::rename(temp, "STUDY_NUMBER" = "Patient")
# fwrite(temp,
# file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.clinicaldata.after_qc.IC_commercial.csv"),
# sep = ",", row.names = FALSE, col.names = TRUE,
# showProgress = TRUE)
# rm(temp)
#
# saveRDS(scRNAseqDataCEA39, file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.Seurat.after_qc.IC_commercial.RDS"))
fwrite(temp2,
file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.samplelist.after_qc.IC_academic.csv"),
sep = ",", row.names = FALSE, col.names = TRUE,
showProgress = TRUE)
rm(temp2)
temp <- dplyr::rename(temp, "STUDY_NUMBER" = "Patient")
fwrite(temp,
file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.clinicaldata.after_qc.IC_academic.csv"),
sep = ",", row.names = FALSE, col.names = TRUE,
showProgress = TRUE)
rm(temp)
saveRDS(scRNAseqDataCEA39, file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.Seurat.after_qc.IC_academic.RDS"))AESCRNA
Quality control
Here review the number of cells per sample, plate, and patients. And plot the ratio’s per sample and study number.
How many cells per type ...?
7 14 20 16 22 23 21 12 19 18 17 15 2 10 11 9 13 5 8 4 6 3 0 1
0 0 0 8 17 19 23 26 55 78 94 112 172 173 176 198 199 242 243 293 297 348 831 846 # cat("\n\nHow many cells per plate ...?")
# sort(table(scRNAseqDataCEA39@meta.data$ID))
# cat("\n\nHow many cells per type per plate ...?")
# table(scRNAseqDataCEA39@meta.data$SCT_snn_res.0.8, scRNAseqDataCEA39@meta.data$ID)
cat("\n\nHow many cells per patient ...?")
How many cells per patient ...?
4530 4440 4605 4653 4472 4458 4455 4496 4601 4502 4501 4571 4448 4477 4459 4520 4602 4489 4495 4545 4480 4447 4500 4513 4535 4676 4486 4470 4487 4546 4488 4521
3 6 7 20 22 35 54 70 70 73 75 76 80 84 94 96 96 97 102 106 112 114 116 123 130 135 137 144 144 144 146 161
4580 4491 4541 4450 4542 4453 4443
163 175 178 205 213 222 422
Visualizing these ratio's per study number and sample ...?UMAPPlot(scRNAseqDataCEA39, label = TRUE, pt.size = 1.25, label.size = 4, group.by = "ident",
repel = TRUE)
ggsave(paste0(PLOT_loc, "/", Today, ".UMAP.png"), plot = last_plot())Saving 7.29 x 4.51 in imageSaving 7.29 x 4.51 in image
# barplot(prop.table(x = table(scRNAseqDataCEA39@active.ident, scRNAseqDataCEA39@meta.data$Patient)),
# cex.axis = 1.0, cex.names = 0.5, las = 1,
# col = uithof_color, xlab = "study number", legend.text = FALSE, args.legend = list(x = "bottom"))
# dev.copy(pdf, paste0(QC_loc, "/", Today, ".cell_ratios_per_sample.pdf"))
# dev.off()
# barplot(prop.table(x = table(scRNAseqDataCEA39@active.ident, scRNAseqDataCEA39@meta.data$ID)),
# cex.axis = 1.0, cex.names = 0.5, las = 2,
# col = uithof_color, xlab = "sample ID", legend.text = FALSE, args.legend = list(x = "bottom"))
# dev.copy(pdf, paste0(QC_loc, "/", Today, ".cell_ratios_per_sample_per_plate.pdf"))
# dev.off()Visualisations
Feature plots of known cellular markers
Let’s project known cellular markers.
UMAPPlot(scRNAseqDataCEA39, label = FALSE, pt.size = 1.25, label.size = 4, group.by = "ident",
repel = TRUE)
# endothelial cells
FeaturePlot(scRNAseqDataCEA39, features = c("CD34"), cols = c("#ECECEC", "#DB003F"))
# macrophages
FeaturePlot(scRNAseqDataCEA39, features = c("CD14", "CD68"), cols = c("#ECECEC", "#DB003F"))
# FeaturePlot(scRNAseqDataCEA39, features = c("CD8"), cols = c("#ECECEC", "#DB003F"))
# b-cells
FeaturePlot(scRNAseqDataCEA39, features = c("CD79A"), cols = c("#ECECEC", "#DB003F"))
Targets of interest:
We check whether the targets genes were sequenced using our method. In case some genes are not available in our data we could filter them here.
[1] "ADAP1" "BAHD1" "CHAF1A" "DISP2" "FAM98B" "GNA11" "GPR176" "KNL1" "MDFIC" "MPND" "NDUFA11" "PIP5K1C" "SUN1" "TLE5" "ZFAND2A"Here we filter - if needed - genes not present in the data; all genes are present though.
gene_list_rm <- c("")
temp = target_genes[!target_genes %in% gene_list_rm]
target_genes_qc <- c(temp)
# gene_list_qc <- gene_list
#
# for debug
# gene_list_qc_replace <- c("MRTFA")
# target_genes_qc <- target_genes
target_genes_qc [1] "ADAP1" "BAHD1" "CHAF1A" "DISP2" "FAM98B" "GNA11" "GPR176" "KNL1" "MDFIC" "MPND" "NDUFA11" "PIP5K1C" "SUN1" "TLE5" "ZFAND2A"Expression in cell communities
library(RColorBrewer)
p1 <- DotPlot(scRNAseqDataCEA39,
features = target_genes_qc,
cols = "RdBu") +
theme(axis.text.x = element_text(angle = 45, hjust=1, size = 8)) +
theme(axis.title.x = element_blank(), axis.title.y = element_blank()) # remove the y- and x-axis labels
p1
ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.png"), plot = last_plot())ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.ps"), plot = last_plot())
ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.pdf"), plot = last_plot())
Let’s group this dotplot based on some grouping.
If we want we can also filter the genes that are not found in the data.
# Assuming 'gene_list_df' is your data.frame with the relevant columns loaded
gene_list_df_filtered <- subset(gene_list_df, Comments != "not found" | is.na(Comments))
gene_list_df_filteredExpression in cell communities grouped
# Separate genes by significance
gene_list_df_filtered_up <- gene_list_df_filtered$GeneSymbol[gene_list_df_filtered$Group == "UP"]
gene_list_df_filtered_down <- gene_list_df_filtered$GeneSymbol[gene_list_df_filtered$Group == "DOWN"]
# Combine the genes, first DOWN then UP (or vice versa based on preference)
ordered_genes_qc <- c(gene_list_df_filtered_up, gene_list_df_filtered_down)library(RColorBrewer)
# Create DotPlot ordered
p1_group <- DotPlot(scRNAseqData,
features = ordered_genes_qc,
cols = "RdBu") +
theme(axis.text.x = element_text(angle = 45, hjust=1, size = 8)) +
theme(axis.title.x = element_blank(), axis.title.y = element_blank()) # remove the y- and x-axis labels
p1_group
# Save the plots
ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.Grouped.png"), plot = p1_group)Saving 7.29 x 4.51 in imageSaving 7.29 x 4.51 in imageSaving 7.29 x 4.51 in image
Plotting gender
We should also plot these data stratified by gender smooth muscle cells.
Create subsets
[1] male female
Levels: female male# Subset Seurat object for Diabetic Patients
male_cells <- WhichCells(scRNAseqDataCEA39, expression = Gender == "male")
scRNAseqDataCEA39_males <- subset(scRNAseqDataCEA39, cells = male_cells)
# Subset Seurat object for Non-Diabetic Patients
female_cells <- WhichCells(scRNAseqDataCEA39, expression = Gender == "female")
scRNAseqDataCEA39_females <- subset(scRNAseqDataCEA39, cells = female_cells)Smooth muscle cells
Attaching package: ‘cowplot’
The following object is masked from ‘package:patchwork’:
align_plots
The following objects are masked from ‘package:sjPlot’:
plot_grid, save_plot
The following object is masked from ‘package:ggpubr’:
get_legend
The following object is masked from ‘package:lubridate’:
stamp# Create DotPlot for males
p1_males_sma <- DotPlot(scRNAseqDataCEA39_males,
features = ordered_genes_qc,
idents = selected_idents_sma,
cols = "RdBu") +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8)) +
theme(axis.title.x = element_blank(), axis.title.y = element_blank()) +
ggtitle("Males")Warning: Only one identity present, the expression values will be not scaled# Create DotPlot for females
p2_females_sma <- DotPlot(scRNAseqDataCEA39_females,
features = ordered_genes_qc,
idents = selected_idents_sma,
cols = "RdBu") +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8)) +
theme(axis.title.x = element_blank(), axis.title.y = element_blank()) +
# theme(axis.title.x = element_blank(),
# axis.title.y = element_blank(),
# axis.text.y = element_blank(), # Remove y-axis text
# axis.ticks.y = element_blank()) + # Remove y-axis ticks
ggtitle("Females")Warning: Only one identity present, the expression values will be not scaled# Combine the two plots into one panel using cowplot
combined_plot_sma <- plot_grid(p1_males_sma, p2_females_sma, ncol = 2)
# Display the combined plot
print(combined_plot_sma)
# Save the plots
ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.ColocGrouped.SMAonly.GenderMales.png"), plot = p1_males_sma)Saving 7.29 x 4.51 in imageggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.ColocGrouped.SMAonly.GenderMales.ps"), plot = p1_males_sma)Saving 7.29 x 4.51 in imageggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.ColocGrouped.SMAonly.GenderMales.pdf"), plot = p1_males_sma)Saving 7.29 x 4.51 in imageggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.ColocGrouped.SMAonly.GenderFemales.png"), plot = p2_females_sma)Saving 7.29 x 4.51 in imageggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.ColocGrouped.SMAonly.GenderFemales.ps"), plot = p2_females_sma)Saving 7.29 x 4.51 in imageggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.ColocGrouped.SMAonly.GenderFemales.pdf"), plot = p2_females_sma)Saving 7.29 x 4.51 in imageggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.ColocGrouped.SMAonly.Gender.png"), plot = combined_plot_sma)Saving 7.29 x 4.51 in imageggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.ColocGrouped.SMAonly.Gender.ps"), plot = combined_plot_sma)Saving 7.29 x 4.51 in imageggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.ColocGrouped.SMAonly.Gender.pdf"), plot = combined_plot_sma)Saving 7.29 x 4.51 in image
FeaturePlot(scRNAseqDataCEA39, features = c(target_genes_qc),
cols = c("#ECECEC", "#DB003F", "#9A3480","#1290D9"),
combine = TRUE)
ggsave(paste0(PLOT_loc, "/", Today, ".FeaturePlot.Targets.png"), plot = last_plot())
# VlnPlot(scRNAseqDataCEA39, features = "DUSP27")
# VlnPlot files
ifelse(!dir.exists(file.path(PLOT_loc, "/VlnPlot")),
dir.create(file.path(PLOT_loc, "/VlnPlot")),
FALSE)[1] TRUEVlnPlot_loc = paste0(PLOT_loc, "/VlnPlot")
for (GENE in target_genes_qc){
print(paste0("Projecting the expression of ", GENE, "."))
vp1 <- VlnPlot(scRNAseqDataCEA39, features = GENE) +
xlab("cell communities") +
ylab(bquote("normalized expression")) +
theme(axis.title.x = element_text(color = "#000000", size = 14, face = "bold"),
axis.title.y = element_text(color = "#000000", size = 14, face = "bold"),
legend.position = "none")
ggsave(paste0(VlnPlot_loc, "/", Today, ".VlnPlot.",GENE,".png"), plot = last_plot())
ggsave(paste0(VlnPlot_loc, "/", Today, ".VlnPlot.",GENE,".ps"), plot = last_plot())
ggsave(paste0(VlnPlot_loc, "/", Today, ".VlnPlot.",GENE,".pdf"), plot = last_plot())
# print(vp1)
}[1] "Projecting the expression of ADAP1."Saving 7 x 7 in image[1] "Projecting the expression of BAHD1."
[1] "Projecting the expression of CHAF1A."
[1] "Projecting the expression of DISP2."
[1] "Projecting the expression of FAM98B."
[1] "Projecting the expression of GNA11."
[1] "Projecting the expression of GPR176."
[1] "Projecting the expression of KNL1."
[1] "Projecting the expression of MDFIC."
[1] "Projecting the expression of MPND."
[1] "Projecting the expression of NDUFA11."
[1] "Projecting the expression of PIP5K1C."
[1] "Projecting the expression of SUN1."
[1] "Projecting the expression of TLE5."
[1] "Projecting the expression of ZFAND2A."Differential expression between cell communities
Here we project genes to only the broad cell communities:
- macrophages
- endothelial cells
- smooth muscle cells
- T-cells
- B-cells
- Mast cells
- NK-cells
- Mixed cells
Macrophages
[1] CD3+ TC I CD3+ TC IV CD34+ EC I CD3+ TC V
[5] CD3+CD56+ NK II CD3+ TC VI CD68+IL18+TLR4+TREM2+ MRes CD3+CD56+ NK I
[9] ACTA2+ SMC CD3+ TC II FOXP3+ TC CD34+ EC II
[13] CD3+ TC III CD68+CD1C+ DC CD68+CASP1+IL1B+SELL MInf CD79A+ BCmem
[17] CD68+ABCA1+OLR1+TREM2+ FC CD68+KIT+ MC CD68+CD4+ Mono CD79+ BCplasma
20 Levels: CD68+CD4+ Mono CD68+IL18+TLR4+TREM2+ MRes CD68+CD1C+ DC CD68+CASP1+IL1B+SELL MInf ... CD79+ BCplasmaComparison between the macrophages cell communities (CD14/CD68+), and all other communities.
MAC.markers <- FindMarkers(object = scRNAseqDataCEA39,
ident.1 = c("CD68+CASP1+IL1B+SELL MInf",
"CD68+CD1C+ DC",
"CD68+CD4+ Mono",
"CD68+IL18+TLR4+TREM2+ MRes",
"CD68+ABCA1+OLR1+TREM2+ FC"),
ident.2 = c(#"CD68+CASP1+IL1B+SELL MInf",
#"CD68+CD1C+ DC",
#"CD68+CD4+ Mono",
#"CD68+IL18+TLR4+TREM2+ MRes",
#"CD68+ABCA1+OLR1+TREM2+ FC",
"CD3+ TC I",
"CD3+ TC II",
"CD3+ TC III",
"CD3+ TC IV",
"CD3+ TC V",
"CD3+ TC VI",
"FOXP3+ TC",
"CD34+ EC I",
"CD34+ EC II",
"ACTA2+ SMC",
"CD3+CD56+ NK I",
"CD3+CD56+ NK II",
"CD68+KIT+ MC",
"CD79+ BCplasma",
"CD79A+ BCmem"))
DT::datatable(MAC.markers)Warning: It seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/DT/server.htmlWarning: It seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/DT/server.htmlMAC_Volcano_TargetsA = EnhancedVolcano(MAC.markers,
lab = rownames(MAC.markers),
x = "avg_log2FC",
y = "p_val_adj",
selectLab = target_genes_qc,
axisLabSize = 12,
xlab = "average fold-change",
title = "Macrophage markers\n(Macrophage communities vs the rest)",
titleLabSize = 14,
pCutoff = 0.05/(nrow(MAC.markers)), # 20552 genes
FCcutoff = 1.25,
pointSize = 1.5,
labSize = 3.0,
legendLabels =c('NS','avg. fold-change','P',
'P & avg. fold-change'),
legendPosition = "right",
legendLabSize = 10,
legendIconSize = 3.0,
drawConnectors = TRUE,
widthConnectors = 0.2,
colConnectors = "#595A5C",
gridlines.major = FALSE,
gridlines.minor = FALSE)
MAC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.MAC.DEG.Targets.pdf"),
plot = MAC_Volcano_TargetsA)
The target results are given below and written to a file.
Smooth muscle cells
Comparison between the smooth muscle cell communities (ACTA2+), and all other communities.
SMC.markers <- FindMarkers(object = scRNAseqDataCEA39,
ident.1 = c("ACTA2+ SMC"),
ident.2 = c("CD68+CASP1+IL1B+SELL MInf",
"CD68+CD1C+ DC",
"CD68+CD4+ Mono",
"CD68+IL18+TLR4+TREM2+ MRes",
"CD68+ABCA1+OLR1+TREM2+ FC",
"CD3+ TC I",
"CD3+ TC II",
"CD3+ TC III",
"CD3+ TC IV",
"CD3+ TC V",
"CD3+ TC VI",
"FOXP3+ TC",
"CD34+ EC I",
"CD34+ EC II",
#"ACTA2+ SMC",
"CD3+CD56+ NK I",
"CD3+CD56+ NK II",
"CD68+KIT+ MC",
"CD79+ BCplasma",
"CD79A+ BCmem"))
DT::datatable(SMC.markers)Warning: It seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/DT/server.htmlWarning: It seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/DT/server.htmlSMC_Volcano_TargetsA = EnhancedVolcano(SMC.markers,
lab = rownames(SMC.markers),
x = "avg_log2FC",
y = "p_val_adj",
selectLab = target_genes_qc,
axisLabSize = 12,
xlab = "average fold-change",
title = "SMC markers\n(SMC communities vs the rest)",
titleLabSize = 14,
pCutoff = 0.05/(nrow(SMC.markers)), # 20552 genes
FCcutoff = 1.25,
pointSize = 1.5,
labSize = 3.0,
legendLabels =c('NS','avg. fold-change','P',
'P & avg. fold-change'),
legendPosition = "right",
legendLabSize = 10,
legendIconSize = 3.0,
drawConnectors = TRUE,
widthConnectors = 0.2,
colConnectors = "#595A5C",
gridlines.major = FALSE,
gridlines.minor = FALSE)
SMC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.SMC.DEG.Targets.pdf"),
plot = SMC_Volcano_TargetsA)
The target results are given below and written to a file.
Endothelial cells
Comparison between the endothelial cell communities (CD34+), and all other communities.
EC.markers <- FindMarkers(object = scRNAseqDataCEA39,
ident.1 = c("CD34+ EC I",
"CD34+ EC II"),
ident.2 = c("CD68+CASP1+IL1B+SELL MInf",
"CD68+CD1C+ DC",
"CD68+CD4+ Mono",
"CD68+IL18+TLR4+TREM2+ MRes",
"CD68+ABCA1+OLR1+TREM2+ FC",
"CD3+ TC I",
"CD3+ TC II",
"CD3+ TC III",
"CD3+ TC IV",
"CD3+ TC V",
"CD3+ TC VI",
"FOXP3+ TC",
# "CD34+ EC I",
# "CD34+ EC II",
"ACTA2+ SMC",
"CD3+CD56+ NK I",
"CD3+CD56+ NK II",
"CD68+KIT+ MC",
"CD79+ BCplasma",
"CD79A+ BCmem"))
DT::datatable(EC.markers)Warning: It seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/DT/server.htmlWarning: It seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/DT/server.htmlEC_Volcano_TargetsA = EnhancedVolcano(EC.markers,
lab = rownames(EC.markers),
x = "avg_log2FC",
y = "p_val_adj",
selectLab = target_genes_qc,
axisLabSize = 12,
xlab = "average fold-change",
title = "Endothelial cell markers\n(EC communities vs the rest)",
titleLabSize = 14,
pCutoff = 0.05/(nrow(EC.markers)), # 20552 genes
FCcutoff = 1.25,
pointSize = 1.5,
labSize = 3.0,
legendLabels =c('NS','avg. fold-change','P',
'P & avg. fold-change'),
legendPosition = "right",
legendLabSize = 10,
legendIconSize = 3.0,
drawConnectors = TRUE,
widthConnectors = 0.2,
colConnectors = "#595A5C",
gridlines.major = FALSE,
gridlines.minor = FALSE)
EC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.EC.DEG.Targets.pdf"),
plot = EC_Volcano_TargetsA)
The target results are given below and written to a file.
T-cells
Comparison between the T-cell communities (CD3/CD4/CD8+), and all other communities.
TC.markers <- FindMarkers(object = scRNAseqDataCEA39,
ident.1 = c("CD3+ TC I",
"CD3+ TC II",
"CD3+ TC III",
"CD3+ TC IV",
"CD3+ TC V",
"CD3+ TC VI",
"FOXP3+ TC"),
ident.2 = c("CD68+CASP1+IL1B+SELL MInf",
"CD68+CD1C+ DC",
"CD68+CD4+ Mono",
"CD68+IL18+TLR4+TREM2+ MRes",
"CD68+ABCA1+OLR1+TREM2+ FC",
# "CD3+ TC I",
# "CD3+ TC II",
# "CD3+ TC III",
# "CD3+ TC IV",
# "CD3+ TC V",
# "CD3+ TC VI",
# "FOXP3+ TC",
"CD34+ EC I",
"CD34+ EC II",
"ACTA2+ SMC",
"CD3+CD56+ NK I",
"CD3+CD56+ NK II",
"CD68+KIT+ MC",
"CD79+ BCplasma",
"CD79A+ BCmem"))
DT::datatable(TC.markers)Warning: It seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/DT/server.htmlWarning: It seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/DT/server.htmlTC_Volcano_TargetsA = EnhancedVolcano(TC.markers,
lab = rownames(TC.markers),
x = "avg_log2FC",
y = "p_val_adj",
selectLab = target_genes_qc,
axisLabSize = 12,
xlab = "average fold-change",
title = "T-cell markers\n(T-cell communities vs the rest)",
titleLabSize = 14,
pCutoff = 0.05/nrow(TC.markers), # 20552 genes
FCcutoff = 1.25,
pointSize = 1.5,
labSize = 3.0,
legendLabels =c('NS','avg. fold-change','P',
'P & avg. fold-change'),
legendPosition = "right",
legendLabSize = 10,
legendIconSize = 3.0,
drawConnectors = TRUE,
widthConnectors = 0.2,
colConnectors = "#595A5C",
gridlines.major = FALSE,
gridlines.minor = FALSE)
TC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.TC.DEG.Targets.pdf"),
plot = TC_Volcano_TargetsA)
The target results are given below and written to a file.
B-cells
Comparison between the B-cell communities (CD79A+), and all other communities.
BC.markers <- FindMarkers(object = scRNAseqDataCEA39,
ident.1 = c("CD79+ BCplasma",
"CD79A+ BCmem"),
ident.2 = c("CD68+CASP1+IL1B+SELL MInf",
"CD68+CD1C+ DC",
"CD68+CD4+ Mono",
"CD68+IL18+TLR4+TREM2+ MRes",
"CD68+ABCA1+OLR1+TREM2+ FC",
"CD3+ TC I",
"CD3+ TC II",
"CD3+ TC III",
"CD3+ TC IV",
"CD3+ TC V",
"CD3+ TC VI",
"FOXP3+ TC",
"CD34+ EC I",
"CD34+ EC II",
"ACTA2+ SMC",
"CD3+CD56+ NK I",
"CD3+CD56+ NK II",
"CD68+KIT+ MC"
# "CD79+ BCplasma",
# "CD79A+ BCmem"
))
DT::datatable(BC.markers)Warning: It seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/DT/server.htmlWarning: It seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/DT/server.htmlBC_Volcano_TargetsA = EnhancedVolcano(BC.markers,
lab = rownames(BC.markers),
x = "avg_log2FC",
y = "p_val_adj",
selectLab = target_genes_qc,
axisLabSize = 12,
xlab = "average fold-change",
title = "B-cell markers\n(B-cell communities vs the rest)",
titleLabSize = 14,
pCutoff = 0.05/nrow(BC.markers), # 20552 genes
FCcutoff = 1.25,
pointSize = 1.5,
labSize = 3.0,
legendLabels =c('NS','avg. fold-change','P',
'P & avg. fold-change'),
legendPosition = "right",
legendLabSize = 10,
legendIconSize = 3.0,
drawConnectors = TRUE,
widthConnectors = 0.2,
colConnectors = "#595A5C",
gridlines.major = FALSE,
gridlines.minor = FALSE)
BC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.BC.DEG.Targets.pdf"),
plot = BC_Volcano_TargetsA)
The target results are given below and written to a file.
Mast cells
Comparison between the mast cell communities (KIT+), and all other communities.
MC.markers <- FindMarkers(object = scRNAseqDataCEA39,
ident.1 = c("CD68+KIT+ MC"),
ident.2 = c("CD68+CASP1+IL1B+SELL MInf",
"CD68+CD1C+ DC",
"CD68+CD4+ Mono",
"CD68+IL18+TLR4+TREM2+ MRes",
"CD68+ABCA1+OLR1+TREM2+ FC",
"CD3+ TC I",
"CD3+ TC II",
"CD3+ TC III",
"CD3+ TC IV",
"CD3+ TC V",
"CD3+ TC VI",
"FOXP3+ TC",
"CD34+ EC I",
"CD34+ EC II",
"ACTA2+ SMC",
"CD3+CD56+ NK I",
"CD3+CD56+ NK II",
# "CD68+KIT+ MC",
"CD79+ BCplasma",
"CD79A+ BCmem"))
DT::datatable(MC.markers)Warning: It seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/DT/server.htmlWarning: It seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/DT/server.htmlMC_Volcano_TargetsA = EnhancedVolcano(MC.markers,
lab = rownames(MC.markers),
x = "avg_log2FC",
y = "p_val_adj",
selectLab = target_genes_qc,
axisLabSize = 12,
xlab = "average fold-change",
title = "Mast cell markers\n(Mast cell communities vs the rest)",
titleLabSize = 14,
pCutoff = 0.05/nrow(MC.markers), # 20552 genes
FCcutoff = 1.25,
pointSize = 1.5,
labSize = 3.0,
legendLabels =c('NS','avg. fold-change','P',
'P & avg. fold-change'),
legendPosition = "right",
legendLabSize = 10,
legendIconSize = 3.0,
drawConnectors = TRUE,
widthConnectors = 0.2,
colConnectors = "#595A5C",
gridlines.major = FALSE,
gridlines.minor = FALSE)
MC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.MC.DEG.Targets.pdf"),
plot = MC_Volcano_TargetsA)
The target results are given below and written to a file.
NK-cells
Comparison between the natural killer cell communities (NCAM1+), and all other communities.
NK.markers <- FindMarkers(object = scRNAseqDataCEA39,
ident.1 = c("CD3+CD56+ NK I",
"CD3+CD56+ NK II"),
ident.2 = c("CD68+CASP1+IL1B+SELL MInf",
"CD68+CD1C+ DC",
"CD68+CD4+ Mono",
"CD68+IL18+TLR4+TREM2+ MRes",
"CD68+ABCA1+OLR1+TREM2+ FC",
"CD3+ TC I",
"CD3+ TC II",
"CD3+ TC III",
"CD3+ TC IV",
"CD3+ TC V",
"CD3+ TC VI",
"FOXP3+ TC",
"CD34+ EC I",
"CD34+ EC II",
"ACTA2+ SMC",
# "CD3+CD56+ NK I",
# "CD3+CD56+ NK II",
"CD68+KIT+ MC",
"CD79+ BCplasma",
"CD79A+ BCmem"))
DT::datatable(NK.markers)Warning: It seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/DT/server.htmlWarning: It seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/DT/server.htmlNK_Volcano_TargetsA = EnhancedVolcano(NK.markers,
lab = rownames(NK.markers),
x = "avg_log2FC",
y = "p_val_adj",
selectLab = target_genes_qc,
axisLabSize = 12,
xlab = "average fold-change",
title = "NK markers\n(NK-cell communities vs the rest)",
titleLabSize = 14,
pCutoff = 0.05/nrow(NK.markers), # 20552 genes
FCcutoff = 1.25,
pointSize = 1.5,
labSize = 3.0,
legendLabels =c('NS','avg. fold-change','P',
'P & avg. fold-change'),
legendPosition = "right",
legendLabSize = 10,
legendIconSize = 3.0,
drawConnectors = TRUE,
widthConnectors = 0.2,
colConnectors = "#595A5C",
gridlines.major = FALSE,
gridlines.minor = FALSE)
NK_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.NK.DEG.Targets.pdf"),
plot = NK_Volcano_TargetsA)
The target results are given below and written to a file.
Session information
Version: v1.2.0
Last update: 2024-10-17
Written by: Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
Description: Script to load single-cell RNA sequencing (scRNAseq) data, and perform quality control (QC), and initial mapping to cells.
Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).
**MoSCoW To-Do List**
The things we Must, Should, Could, and Would have given the time we have.
_M_
_S_
_C_
_W_
**Changes log**
* v1.2.0 Fixed an issue where the subset of scRNAseq was loosing cell-identities.
* v1.1.1 Textual fixes.
* v1.1.1 Fix writing baseline table.
* v1.1.0 Update to study database.
* v1.0.2 Fixes to the start of the notebook. Update to loading of the clinical data. Fix on the gene-filtering.
* v1.0.1 Update to main AEDB (there is an error in the Age-variable in the new version). Fewer patients in scRNAseq (32 vs 39 with the newer dataset).
* v1.0.0 Initial version.R version 4.4.1 (2024-06-14)
Platform: x86_64-apple-darwin20
Running under: macOS 15.1
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/New_York
tzcode source: internal
attached base packages:
[1] stats4 grid tools stats graphics grDevices utils datasets methods base
other attached packages:
[1] cowplot_1.1.3 RColorBrewer_1.1-3 rmarkdown_2.28 Seurat_5.1.0 SeuratObject_5.0.2
[6] sp_2.1-4 BiocManager_1.30.25 EnhancedVolcano_1.22.0 ggrepel_0.9.6 mygene_1.40.0
[11] txdbmaker_1.0.1 GenomicFeatures_1.56.0 GenomicRanges_1.56.2 GenomeInfoDb_1.40.1 org.Hs.eg.db_3.19.1
[16] AnnotationDbi_1.66.0 IRanges_2.38.1 S4Vectors_0.42.1 Biobase_2.64.0 BiocGenerics_0.50.0
[21] tidylog_1.1.0 patchwork_1.3.0.9000 labelled_2.13.0 sjPlot_2.8.16 UpSetR_1.4.0
[26] ggpubr_0.6.0.999 forestplot_3.1.5 abind_1.4-8 checkmate_2.3.2 pheatmap_1.0.12
[31] devtools_2.4.5 usethis_3.0.0 BlandAltmanLeh_0.3.1 tableone_0.13.2 openxlsx_4.2.7.1
[36] haven_2.5.4 eeptools_1.2.5 DT_0.33 knitr_1.48 lubridate_1.9.3
[41] forcats_1.0.0 stringr_1.5.1 purrr_1.0.2 tibble_3.2.1 ggplot2_3.5.1
[46] tidyverse_2.0.0 data.table_1.16.2 naniar_1.1.0 tidyr_1.3.1 dplyr_1.1.4
[51] optparse_1.7.5 readr_2.1.5 pander_0.6.5 R.utils_2.12.3 R.oo_1.26.0
[56] R.methodsS3_1.8.2 worcs_0.1.15 credentials_2.0.2
loaded via a namespace (and not attached):
[1] sjmisc_2.8.10 progress_1.2.3 urlchecker_1.0.1 nnet_7.3-19
[5] goftest_1.2-3 Biostrings_2.72.1 vctrs_0.6.5 spatstat.random_3.3-2
[9] rticles_0.27 digest_0.6.37 png_0.1-8 proxy_0.4-27
[13] deldir_2.0-4 parallelly_1.38.0 renv_1.0.11 MASS_7.3-61
[17] reshape2_1.4.4 httpuv_1.6.15 withr_3.0.1 ggrastr_1.0.2
[21] xfun_0.48 ellipsis_0.3.2 survival_3.7-0 memoise_2.0.1
[25] ggbeeswarm_0.7.2 profvis_0.4.0 systemfonts_1.1.0 ragg_1.3.3
[29] zoo_1.8-12 pbapply_1.7-2 Formula_1.2-5 sys_3.4.3
[33] prettyunits_1.2.0 datawizard_0.13.0 KEGGREST_1.44.1 promises_1.3.0
[37] httr_1.4.7 rstatix_0.7.2 restfulr_0.0.15 globals_0.16.3
[41] fitdistrplus_1.2-1 rstudioapi_0.16.0 UCSC.utils_1.0.0 miniUI_0.1.1.1
[45] generics_0.1.3 base64enc_0.1-3 curl_5.2.3 mitools_2.4
[49] zlibbioc_1.50.0 polyclip_1.10-7 ggeffects_1.7.2 GenomeInfoDbData_1.2.12
[53] SparseArray_1.4.8 xtable_1.8-4 evaluate_1.0.1 S4Arrays_1.4.1
[57] BiocFileCache_2.12.0 hms_1.1.3 irlba_2.3.5.1 colorspace_2.1-1
[61] filelock_1.0.3 getopt_1.20.4 ROCR_1.0-11 spatstat.data_3.1-2
[65] reticulate_1.39.0 magrittr_2.0.3 lmtest_0.9-40 later_1.3.2
[69] lattice_0.22-6 spatstat.geom_3.3-3 future.apply_1.11.2 scattermore_1.2
[73] XML_3.99-0.17 survey_4.4-2 matrixStats_1.4.1 RcppAnnoy_0.0.22
[77] class_7.3-22 Hmisc_5.1-3 pillar_1.9.0 nlme_3.1-166
[81] performance_0.12.3 compiler_4.4.1 RSpectra_0.16-2 stringi_1.8.4
[85] tensor_1.5 minqa_1.2.8 SummarizedExperiment_1.34.0 GenomicAlignments_1.40.0
[89] plyr_1.8.9 crayon_1.5.3 BiocIO_1.14.0 chron_2.3-61
[93] bit_4.5.0 textshaping_0.4.0 codetools_0.2-20 clisymbols_1.2.0
[97] openssl_2.2.2 crosstalk_1.2.1 bslib_0.8.0 e1071_1.7-16
[101] plotly_4.10.4 mime_0.12 splines_4.4.1 Rcpp_1.0.13
[105] fastDummies_1.7.4 dbplyr_2.5.0 blob_1.2.4 utf8_1.2.4
[109] lme4_1.1-35.5 fs_1.6.4 listenv_0.9.1 prereg_0.6.0
[113] pkgbuild_1.4.4 gh_1.4.1 ggsignif_0.6.4 sqldf_0.4-11
[117] Matrix_1.7-0 statmod_1.5.0 tzdb_0.4.0 visdat_0.6.0
[121] pkgconfig_2.0.3 cachem_1.1.0 RSQLite_2.3.7 viridisLite_0.4.2
[125] DBI_1.2.3 fastmap_1.2.0 scales_1.3.0 ica_1.0-3
[129] Rsamtools_2.20.0 broom_1.0.7 sass_0.4.9 coda_0.19-4.1
[133] insight_0.20.5 dotCall64_1.2 carData_3.0-5 RANN_2.6.2
[137] rpart_4.1.23 farver_2.1.2 gsubfn_0.7 yaml_2.3.10
[141] MatrixGenerics_1.16.0 foreign_0.8-87 sjstats_0.19.0 rtracklayer_1.64.0
[145] cli_3.6.3 leiden_0.4.3.1 lifecycle_1.0.4 uwot_0.2.2
[149] askpass_1.2.1 presto_1.0.0 sessioninfo_1.2.2 backports_1.5.0
[153] BiocParallel_1.38.0 timechange_0.3.0 gtable_0.3.5 rjson_0.2.23
[157] ggridges_0.5.6 progressr_0.14.0 limma_3.60.6 parallel_4.4.1
[161] jsonlite_1.8.9 RcppHNSW_0.6.0 bitops_1.0-9 bit64_4.5.2
[165] Rtsne_0.17 spatstat.utils_3.1-0 proto_1.0.0 zip_2.3.1
[169] ranger_0.16.0 jquerylib_0.1.4 spatstat.univar_3.0-1 lazyeval_0.2.2
[173] shiny_1.9.1 htmltools_0.5.8.1 sctransform_0.4.1 rappdirs_0.3.3
[177] sjlabelled_1.2.0 tinytex_0.53 glue_1.8.0 spam_2.11-0
[181] httr2_1.0.5 XVector_0.44.0 RCurl_1.98-1.16 gridExtra_2.3
[185] boot_1.3-31 igraph_2.0.3 R6_2.5.1 arm_1.14-4
[189] vcd_1.4-13 labeling_0.4.3 cluster_2.1.6 pkgload_1.4.0
[193] nloptr_2.1.1 vipor_0.4.7 DelayedArray_0.30.1 tidyselect_1.2.1
[197] htmlTable_2.4.3 xml2_1.3.6 car_3.1-3 future_1.34.0
[201] munsell_0.5.1 KernSmooth_2.23-24 htmlwidgets_1.6.4 spatstat.sparse_3.1-0
[205] biomaRt_2.60.1 rlang_1.1.4 spatstat.explore_3.3-2 gert_2.1.4
[209] remotes_2.5.0 fansi_1.0.6 beeswarm_0.4.0 Saving environment
rm(backup.scRNAseqData)
rm(scRNAseqData, scRNAseqDataCEA39)
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".AESCRNA.results.RData"))| © 1979-2024 Sander W. van der Laan | s.w.vanderlaan[at]gmail[dot]com | vanderlaanand.science. |
---
title: "Mapping targets to single cells in plaques."
subtitle: Accompanying 'MR_CVD_MDD'
author: '[Sander W. van der Laan, PhD](https://vanderlaanand.science) | s.w.vanderlaan[at]gmail[dot]com'
date: '`r Sys.Date()`'
output:
  html_notebook: 
    cache: yes
    code_folding: hide
    collapse: yes
    df_print: paged
    fig.align: center
    fig_caption: yes
    fig_height: 10
    fig_retina: 2
    fig_width: 12
    theme: paper
    toc: yes
    toc_float:
      collapsed: no
      smooth_scroll: yes
    highlight: tango
mainfont: Helvetica
editor_options:
  chunk_output_type: inline
bibliography: references.bib
knit: worcs::cite_all
---

# General Setup
```{r echo = FALSE}
rm(list = ls())
```

```{r LocalSystem, echo = FALSE}
source("scripts/local.system.R")
```

```{r Source functions}
source("scripts/functions.R")
```

```{r loading_packages, message=FALSE, warning=FALSE}
source("scripts/pack03.packages.R")
```

```{r Setting: Colors}
Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")
source("scripts/colors.R")
```

```{r setup_notebook, include=FALSE}
# We recommend that you prepare your raw data for analysis in 'prepare_data.R',
# and end that file with either open_data(yourdata), or closed_data(yourdata).
# Then, uncomment the line below to load the original or synthetic data
# (whichever is available), to allow anyone to reproduce your code:
# load_data()

# further define some knitr-options.
knitr::opts_chunk$set(fig.width = 12, fig.height = 8, fig.path = 'Figures/', 
                      warning = TRUE, # show warnings during codebook generation
                      message = TRUE, # show messages during codebook generation
                      error = TRUE, # do not interrupt codebook generation in case of errors, 
                                    # usually better for debugging
                      echo = TRUE,  # show R code
                      eval = TRUE)

ggplot2::theme_set(ggplot2::theme_minimal())
# pander::panderOptions("table.split.table", Inf)
library("worcs")
library("rmarkdown")

```

# ERA-CVD 'druggable-MI-targets'

<!-- ![ERA-CVD logo]("Users/swvanderlaan/iCloud/Genomics/Projects/#Druggable-MI-Genes/Administration/ERA-CVD\ Logo_CMYK.jpg") -->

For the ERA-CVD 'druggable-MI-targets' project (grantnumber: 01KL1802) we performed two related RNA sequencing (RNAseq) experiments:

1)  conventional ('bulk') RNAseq using RNA extracted from carotid plaque samples, n ± 700. As of `r Today.Report` all samples have been selected and
RNA has been extracted; quality control (QC) was performed and we have a dataset of 635 samples.

2)  single-cell RNAseq (scRNAseq) of at least n = 40 samples (20 females, 20 males). As of `r Today.Report` data is available of 40 samples (3 females, 15 males), we are extending sampling to get more female samples.

Plaque samples are derived from carotid endarterectomies as part of the [Athero-Express Biobank Study](http:www/atheroexpress.nl) which is an ongoing study in the UMC Utrecht.

# Background

Here we map the `r TRAIT_OF_INTEREST` to single-cells from the plaques.

## Targets

Here we obtain data from the `r TRAIT_OF_INTEREST` in plaques.

```{r targets, message=FALSE, warning=FALSE}
library(openxlsx)

gene_list_df <- read.xlsx(paste0(PROJECT_loc, "/targets/targets.xlsx"), sheet = "Genes")

gene_list <- unlist(gene_list_df$Gene)
gene_list

```


# Load data

First we will load the data:

-   scRNAseq experimental data and rename the cell types.
-   Athero-Express clinical data.

Here we load the latest dataset from our Athero-Express single-cell RNA experiment.

```{r LoadData}

# load(paste0(AESCRNA_loc, "/20210811.46.patients.KP.RData"))
# scRNAseqData <- seuset
# rm(seuset)
# 
# saveRDS(scRNAseqData, paste0(AESCRNA_loc, "/20210811.46.patients.KP.RDS"))

scRNAseqData <- readRDS(paste0(AESCRNA_loc, "/20210811.46.patients.KP.RDS"))

scRNAseqData

```

The naming/classification is based on a combination conventional markers. We do not claim to know the exact identity of each cell, rather we refer to cells as 'KIT+ Mast cells"-like cells. Likewise we refer to the cell clusters as 'communities' of cells that exhibit similar properties, *i.e.* similar defining markers (*e.g. KIT*).

We will rename the cell types to human readable names.

```{r Change cell cummunity names}
### change names for clarity
backup.scRNAseqData = scRNAseqData
# get the old names to change to new names
UMAPPlot(scRNAseqData, label = FALSE, pt.size = 1.25, label.size = 4, group.by = "ident")

```

```{r}
unique(scRNAseqData@active.ident)
```

```{r}
celltypes <- c("CD68+CD4+ Monocytes" = "CD68+CD4+ Mono", 
               "CD68+IL18+TLR4+TREM2+ Resident macrophages" = "CD68+IL18+TLR4+TREM2+ MRes", 
               "CD68+CD1C+ Dendritic Cells" = "CD68+CD1C+ DC",
               "CD68+CASP1+IL1B+SELL+ Inflammatory macrophages" = "CD68+CASP1+IL1B+SELL MInf",
               "CD68+ABCA1+OLR1+TREM2+ Foam Cells" = "CD68+ABCA1+OLR1+TREM2+ FC",
               
               # T-cells
               "CD3+ T Cells I" = "CD3+ TC I",
               "CD3+ T Cells II" = "CD3+ TC II", 
               "CD3+ T Cells III" = "CD3+ TC III", 
               "CD3+ T Cells IV" = "CD3+ TC IV", 
               "CD3+ T Cells V" = "CD3+ TC V", 
               "CD3+ T Cells VI" = "CD3+ TC VI", 
               "FOXP3+ T Cells" = "FOXP3+ TC",
               
               # Endothelial cells
               "CD34+ Endothelial Cells I" = "CD34+ EC I", 
               "CD34+ Endothelial Cells II" = "CD34+ EC II", 
               
               # SMC
               "ACTA2+ Smooth Muscle Cells" = "ACTA2+ SMC", 
               
               # NK Cells
               "CD3+CD56+ NK Cells I" = "CD3+CD56+ NK I",
               "CD3+CD56+ NK Cells II" = "CD3+CD56+ NK II",
               # Mast
               "CD68+KIT+ Mast Cells" = "CD68+KIT+ MC",
               
               "CD79A+ Class-switched Memory B Cells" = "CD79A+ BCmem", 
               "CD79+ Plasma B Cells" = "CD79+ BCplasma")

scRNAseqData <- Seurat::RenameIdents(object = scRNAseqData, 
                                       celltypes)
```

```{r Change cell cummunity names - new plot}
UMAPPlot(scRNAseqData, label = TRUE, pt.size = 1.25, label.size = 4, group.by = "ident",
         repel = TRUE)

```

## Clinical data

Loading the Athero-Express clinical data.

```{r LoadAEDB}
AEDB.CEA <- readRDS(file = paste0(OUT_loc, "/", Today,".",PROJECTNAME,".AEDB.CEA.RDS"))
# AEDB.CEA <- readRDS(file = paste0(OUT_loc, "/20240531.",PROJECTNAME,".AEDB.CEA.RDS"))

```


```{r }
# Baseline table variables
basetable_vars = c("Hospital", "ORyear", "Artery_summary",
                   "Age", "Gender", 
                   # "TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU", 
                   "TC_final", "LDL_final", "HDL_final", "TG_final", 
                   # "hsCRP_plasma",
                   "systolic", "diastoli", "GFR_MDRD", "BMI", 
                   "KDOQI", "BMI_WHO",
                   "SmokerStatus", "AlcoholUse",
                   "DiabetesStatus", 
                   "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                   "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                   "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                   "Symptoms.Update2G", "Symptoms.Update3G", "indexsymptoms_latest_4g",
                   "restenos", "stenose", 
                   "CAD_history", "PAOD", "Peripheral.interv", 
                   "EP_composite", "EP_composite_time", "EP_major", "EP_major_time",
                   "MAC_rankNorm", "SMC_rankNorm", "Macrophages.bin", "SMC.bin",
                   "Neutrophils_rankNorm", "MastCells_rankNorm",
                   "IPH.bin", "VesselDensity_rankNorm",
                   "Calc.bin", "Collagen.bin", 
                   "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype", "Plaque_Vulnerability_Index",
                   "PCSK9_plasma", "PCSK9_plasma_rankNorm")

basetable_bin = c("Gender",  "Artery_summary",
                  "KDOQI", "BMI_WHO",
                  "SmokerStatus", "AlcoholUse",
                  "DiabetesStatus", 
                  "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                  "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                  "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                  "Symptoms.Update2G", "Symptoms.Update3G", "indexsymptoms_latest_4g",
                  "restenos", "stenose",
                  "CAD_history", "PAOD", "Peripheral.interv", 
                  "EP_major", "EP_composite", "Macrophages.bin", "SMC.bin",
                  "IPH.bin", 
                  "Calc.bin", "Collagen.bin", 
                  "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype", "Plaque_Vulnerability_Index")
# basetable_bin

basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_con
```

## AESCRNA: baseline characteristics

### Preparation

```{r Baseline: creation}
metadata <- scRNAseqData@meta.data %>% as_tibble() %>% separate(orig.ident, c("Patient", NA))
scRNAseqDataMeta <- metadata %>% distinct(Patient, .keep_all = TRUE)

scRNAseqDataMetaAE <- merge(scRNAseqDataMeta, AEDB.CEA, by.x = "Patient", by.y = "STUDY_NUMBER", sort = FALSE, all.x = TRUE)
dim(scRNAseqDataMetaAE)

# Replace missing data 
# Ref: https://cran.r-project.org/web/packages/naniar/vignettes/replace-with-na.html
require(naniar)

na_strings <- c("NA", "N A", "N / A", "N/A", "N/ A", 
                "Not Available", "Not available", 
                "missing", 
                "-999", "-99", 
                "No data available/missing", "No data available/Missing")
# Then you write ~.x %in% na_strings - which reads as “does this value occur in the list of NA strings”.

scRNAseqDataMetaAE %>%
  replace_with_na_all(condition = ~.x %in% na_strings)
```

```{r }
cat("====================================================================================================")
cat("SELECTION THE SHIZZLE")

cat("- sanity checking PRIOR to selection")
library(data.table)
require(labelled)
ae.gender <- to_factor(scRNAseqDataMetaAE$Gender)
ae.hospital <- to_factor(scRNAseqDataMetaAE$Hospital)
table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"), useNA = "ifany")

ae.artery <- to_factor(scRNAseqDataMetaAE$Artery_summary)
table(ae.artery, ae.gender, dnn = c("Sex", "Artery"), useNA = "ifany")

ae.ic <- to_factor(scRNAseqDataMetaAE$informedconsent)
table(ae.ic, ae.gender, useNA = "ifany")

rm(ae.gender, ae.hospital, ae.artery, ae.ic)


scRNAseqDataMetaAE.all <- subset(scRNAseqDataMetaAE,
                                 (Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)" ) & # we only want carotids
                                   informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
                                   informedconsent != "no, died" &
                                   informedconsent != "yes, no tissue, no commerical business" &
                                   informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
                                   informedconsent != "yes, no tissue, no health treatment" &
                                   informedconsent != "yes, no tissue, no questionnaires" &
                                   informedconsent != "yes, no tissue, health treatment when possible" &
                                   informedconsent != "yes, no tissue" &
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
                                   informedconsent != "no, doesn't want to" &
                                   informedconsent != "no, unable to sign" &
                                   informedconsent != "no, no reaction" &
                                   informedconsent != "no, lost" &
                                   informedconsent != "no, too old" &
                                   informedconsent != "yes, no medical info, health treatment when possible" & 
                                   informedconsent != "no (never asked for IC because there was no tissue)" &
                                   informedconsent != "no, endpoint" &
                                   informedconsent != "nooit geincludeerd" & 
                                   informedconsent != "yes, no health treatment, no commercial business" & # IMPORTANT: since we are sharing with a commercial party
                                   informedconsent != "yes, no tissue, no commerical business" & 
                                   informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" & 
                                   informedconsent != "yes, no questionnaires, no health treatment, no commercial business" & 
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" & 
                                   informedconsent != "yes, no health treatment, no medical info, no commercial business" & 
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" & 
                                   informedconsent != "yes, no commerical business" & 
                                   informedconsent != "yes, health treatment when possible, no commercial business" & 
                                   informedconsent != "yes, no medical info, no commercial business" & 
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" & 
                                   informedconsent != "yes, no questionnaires, no commercial business" & 
                                   informedconsent != "yes, no questionnaires, health treatment when possible, no commercial business" & 
                                   informedconsent != "second informed concents: yes, no commercial business")
# scRNAseqDataMetaAE.all[1:10, 1:10]
dim(scRNAseqDataMetaAE.all)
# DT::datatable(scRNAseqDataMetaAE.all)

```

### Baseline

Showing the baseline table for the scRNAseq data in 39 CEA patients with
informed consent.

```{r Baseline: Visualize}
cat("===========================================================================================")
cat("CREATE BASELINE TABLE")

# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
scRNAseqDataMetaAE.all.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                                  # factorVars = basetable_bin,
                                                  # strata = "Gender",
                                                  data = scRNAseqDataMetaAE.all, includeNA = TRUE), 
                                   nonnormal = c(), 
                                   quote = FALSE, showAllLevels = TRUE,
                                   format = "p", 
                                   contDigits = 3)[,1:2]

```

Writing the baseline table to Excel format.

```{r }
# Write basetable
require(openxlsx)
# write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AESCRNA.32pts.after_qc.IC_commercial.BaselineTable.xlsx"), 
#            format(as.data.frame(scRNAseqDataMetaAE.all.tableOne), digits = 5, scientific = FALSE),
#            rowNames = TRUE, colNames = TRUE, 
#            sheetName = "AESCRNA", overwrite = TRUE)
write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AESCRNA.CEA.39pts.after_qc.IC_academic.BaselineTable.xlsx"),
           format(as.data.frame(scRNAseqDataMetaAE.all.tableOne), digits = 5, scientific = FALSE),
           rowNames = TRUE, colNames = TRUE,
           sheetName = "AESCRNA_CEA", overwrite = TRUE)

```


# Subset scRNAseq data

List of samples to be included based on informed consent (see above).

```{r}
samples_of_interest <- unlist(scRNAseqDataMetaAE.all$Patient)
samples_of_interest
```

Just make sure to keep the rows (`patientid`.`plateid`.`cellid`) in the data, so we will create a new column for that.

```{r}
scRNAseqData$cell.ident <- rownames(scRNAseqData[[]])
```

Subsetting the samples of interest based on `samples_of_interest`.

```{r}
scRNAseqDataCEA39 <- subset(scRNAseqData, subset = Patient %in% samples_of_interest)
```


Selecting the clinical data of interest.

```{r}
variables_of_interest <- c("Hospital", "ORyear", "Artery_summary",
                           "Age", "Gender",
                           "TC_final", "LDL_final", "HDL_final", "TG_final",
                           "systolic", "diastoli", "GFR_MDRD", "BMI",
                           "KDOQI", "BMI_WHO",
                           "SmokerStatus", "AlcoholUse",
                           "DiabetesStatus",
                           "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs",
                           "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
                           "Stroke_Dx",
                           "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                           "Symptoms.Update2G", "Symptoms.Update3G", "indexsymptoms_latest_4g",
                           "restenos", "stenose",
                           "CAD_history", "PAOD", "Peripheral.interv",
                           "EP_composite", "EP_composite_time", "EP_major", "EP_major_time")

temp <- subset(scRNAseqDataMetaAE.all, select = c("Patient", variables_of_interest))
```

Now we are merging in the clinical data of interest.

```{r}
# Step 1: Extract original metadata from Seurat object
original_metadata_df <- scRNAseqDataCEA39@meta.data

# Step 2: Merge with dataframe (temp) with select variables based on 'Patient' column
merged_metadata_df <- merge(original_metadata_df, temp, by = "Patient", all.x = TRUE, sort = FALSE)

# Step 3: Exclude already existing columns to avoid duplication
new_metadata_columns <- setdiff(colnames(temp), colnames(original_metadata_df))
new_meta_info_df <- merged_metadata_df[, new_metadata_columns, drop = FALSE]

# Step 4: Add the new metadata to the Seurat object
scRNAseqDataCEA39 <- AddMetaData(scRNAseqDataCEA39, metadata = new_meta_info_df)

# Step 5: Rename `Patient` to `STUDY_NUMBER` to match the clinical data
scRNAseqDataCEA39@meta.data <- dplyr::rename(scRNAseqDataCEA39@meta.data, "STUDY_NUMBER" = "Patient")

# Verify that the new metadata was added correctly
head(scRNAseqDataCEA39@meta.data)

```

## Saving new dataset

```{r}
temp2 <- as_tibble(subset(scRNAseqDataCEA39@meta.data, select = c("STUDY_NUMBER", "orig.ident", "nCount_RNA", "nFeature_RNA",
                                                                 "Plate", "Batch", "C.H", "Type", "percent.mt",
                                                                 "nCount_SCT", "nFeature_SCT", "seurat_clusters")))

# fwrite(temp2,
#        file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.samplelist.after_qc.IC_commercial.csv"),
#        sep = ",", row.names = FALSE, col.names = TRUE,
#        showProgress = TRUE)
# rm(temp2)
# 
# temp <- dplyr::rename(temp, "STUDY_NUMBER" = "Patient")
# fwrite(temp,
#        file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.clinicaldata.after_qc.IC_commercial.csv"),
#        sep = ",", row.names = FALSE, col.names = TRUE,
#        showProgress = TRUE)
# rm(temp)
# 
# saveRDS(scRNAseqDataCEA39, file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.Seurat.after_qc.IC_commercial.RDS"))

fwrite(temp2,
       file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.samplelist.after_qc.IC_academic.csv"),
       sep = ",", row.names = FALSE, col.names = TRUE,
       showProgress = TRUE)
rm(temp2)

temp <- dplyr::rename(temp, "STUDY_NUMBER" = "Patient")
fwrite(temp,
       file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.clinicaldata.after_qc.IC_academic.csv"),
       sep = ",", row.names = FALSE, col.names = TRUE,
       showProgress = TRUE)
rm(temp)

saveRDS(scRNAseqDataCEA39, file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.Seurat.after_qc.IC_academic.RDS"))

```


# AESCRNA

## Quality control

Here review the number of cells per sample, plate, and patients. And plot the
ratio's per sample and study number.

```{r QualityControl}
## check stuff
cat("\nHow many cells per type ...?")
sort(table(scRNAseqDataCEA39@meta.data$SCT_snn_res.1.25))

# cat("\n\nHow many cells per plate ...?")
# sort(table(scRNAseqDataCEA39@meta.data$ID))

# cat("\n\nHow many cells per type per plate ...?")
# table(scRNAseqDataCEA39@meta.data$SCT_snn_res.0.8, scRNAseqDataCEA39@meta.data$ID)

cat("\n\nHow many cells per patient ...?")
sort(table(scRNAseqDataCEA39@meta.data$STUDY_NUMBER))

cat("\n\nVisualizing these ratio's per study number and sample ...?")
UMAPPlot(scRNAseqDataCEA39, label = TRUE, pt.size = 1.25, label.size = 4, group.by = "ident",
         repel = TRUE)
ggsave(paste0(PLOT_loc, "/", Today, ".UMAP.png"), plot = last_plot())
ggsave(paste0(PLOT_loc, "/", Today, ".UMAP.ps"), plot = last_plot())


# barplot(prop.table(x = table(scRNAseqDataCEA39@active.ident, scRNAseqDataCEA39@meta.data$Patient)), 
#         cex.axis = 1.0, cex.names = 0.5, las = 1,
#         col = uithof_color, xlab = "study number", legend.text = FALSE, args.legend = list(x = "bottom"))
# dev.copy(pdf, paste0(QC_loc, "/", Today, ".cell_ratios_per_sample.pdf"))
# dev.off()

# barplot(prop.table(x = table(scRNAseqDataCEA39@active.ident, scRNAseqDataCEA39@meta.data$ID)), 
#         cex.axis = 1.0, cex.names = 0.5, las = 2,
#         col = uithof_color, xlab = "sample ID", legend.text = FALSE, args.legend = list(x = "bottom"))
# dev.copy(pdf, paste0(QC_loc, "/", Today, ".cell_ratios_per_sample_per_plate.pdf"))
# dev.off()

```

## Visualisations

### Feature plots of known cellular markers

Let's project known cellular markers.

```{r Visualisation: tSNE Exploration}
UMAPPlot(scRNAseqDataCEA39, label = FALSE, pt.size = 1.25, label.size = 4, group.by = "ident",
         repel = TRUE)

# endothelial cells
FeaturePlot(scRNAseqDataCEA39, features = c("CD34"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqDataCEA39, features = c("EDN1"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqDataCEA39, features = c("EDNRA", "EDNRB"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqDataCEA39, features = c("CDH5", "PECAM1"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqDataCEA39, features = c("ACKR1"), cols =  c("#ECECEC", "#DB003F"))

# SMC
FeaturePlot(scRNAseqDataCEA39, features = c("MYH11"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqDataCEA39, features = c("LGALS3", "ACTA2"), cols =  c("#ECECEC", "#DB003F"))

# macrophages
FeaturePlot(scRNAseqDataCEA39, features = c("CD14", "CD68"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqDataCEA39, features = c("CD36"), cols =  c("#ECECEC", "#DB003F"))

# t-cells
FeaturePlot(scRNAseqDataCEA39, features = c("CD3E"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqDataCEA39, features = c("CD4"), cols =  c("#ECECEC", "#DB003F"))
# FeaturePlot(scRNAseqDataCEA39, features = c("CD8"), cols =  c("#ECECEC", "#DB003F"))

# b-cells
FeaturePlot(scRNAseqDataCEA39, features = c("CD79A"), cols =  c("#ECECEC", "#DB003F"))

# mast cells
FeaturePlot(scRNAseqDataCEA39, features = c("KIT"), cols =  c("#ECECEC", "#DB003F"))

# NK cells
FeaturePlot(scRNAseqDataCEA39, features = c("NCAM1"), cols =  c("#ECECEC", "#DB003F"))

```

## Targets of interest:

We check whether the targets genes were sequenced using our method. In case some genes are not available in our data we could filter them here.

```{r list target genes}
target_genes <- gene_list
target_genes
```


Here we filter - if needed - genes not present in the data; all genes are present though. 

```{r Visualisation: preparation}

gene_list_rm <- c("") 

temp = target_genes[!target_genes %in% gene_list_rm]

target_genes_qc <- c(temp)

# gene_list_qc <- gene_list
# 
# for debug
# gene_list_qc_replace <- c("MRTFA")

# target_genes_qc <- target_genes
target_genes_qc

```


### Expression in cell communities

```{r Visualisation: Targets Dot Plots, message=FALSE, warning=FALSE}
library(RColorBrewer)

p1 <- DotPlot(scRNAseqDataCEA39, 
              features = target_genes_qc,
              cols = "RdBu") + 
  theme(axis.text.x = element_text(angle = 45, hjust=1, size = 8)) +
  theme(axis.title.x = element_blank(), axis.title.y = element_blank()) # remove the y- and x-axis labels

p1

ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.png"), plot = last_plot())
ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.ps"), plot = last_plot())
ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.pdf"), plot = last_plot())

rm(p1)
```

Let's group this dotplot based on some grouping.

If we want we can also filter the genes that are not found in the data.

```{r}
# Assuming 'gene_list_df' is your data.frame with the relevant columns loaded
gene_list_df_filtered <- subset(gene_list_df, Comments != "not found" | is.na(Comments))
gene_list_df_filtered
```

### Expression in cell communities grouped

```{r}
# Separate genes by significance
gene_list_df_filtered_up <- gene_list_df_filtered$GeneSymbol[gene_list_df_filtered$Group == "UP"]
gene_list_df_filtered_down <- gene_list_df_filtered$GeneSymbol[gene_list_df_filtered$Group == "DOWN"]

# Combine the genes, first DOWN then UP (or vice versa based on preference)
ordered_genes_qc <- c(gene_list_df_filtered_up, gene_list_df_filtered_down)
```


```{r}
library(RColorBrewer)
# Create DotPlot ordered
p1_group <- DotPlot(scRNAseqData, 
              features = ordered_genes_qc, 
              cols = "RdBu") + 
  theme(axis.text.x = element_text(angle = 45, hjust=1, size = 8)) +
  theme(axis.title.x = element_blank(), axis.title.y = element_blank()) # remove the y- and x-axis labels

p1_group

# Save the plots
ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.Grouped.png"), plot = p1_group)
ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.Grouped.ps"), plot = p1_group)
ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.Grouped.pdf"), plot = p1_group)

```

#### Plotting gender

We should also plot these data stratified by gender smooth muscle cells.

##### Create subsets

```{r}
unique(scRNAseqDataCEA39$Gender)
```

```{r}
# Subset Seurat object for Diabetic Patients
male_cells <- WhichCells(scRNAseqDataCEA39, expression = Gender == "male")
scRNAseqDataCEA39_males <- subset(scRNAseqDataCEA39, cells = male_cells)

# Subset Seurat object for Non-Diabetic Patients
female_cells <- WhichCells(scRNAseqDataCEA39, expression = Gender == "female")
scRNAseqDataCEA39_females <- subset(scRNAseqDataCEA39, cells = female_cells)

```

##### Smooth muscle cells

```{r}
# Define the cell identities you want to include in the plot
selected_idents_sma <- c("ACTA2+ SMC")
```

```{r}
library(RColorBrewer)
library(cowplot)  # For combining plots

# Create DotPlot for males
p1_males_sma <- DotPlot(scRNAseqDataCEA39_males,
                       features = ordered_genes_qc,
                       idents = selected_idents_sma, 
                       
                       cols = "RdBu") + 
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8)) +
  theme(axis.title.x = element_blank(), axis.title.y = element_blank()) + 
  ggtitle("Males")

# Create DotPlot for females
p2_females_sma <- DotPlot(scRNAseqDataCEA39_females,
                           features = ordered_genes_qc,
                           idents = selected_idents_sma,
                           
                           cols = "RdBu") + 
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8)) +
  theme(axis.title.x = element_blank(), axis.title.y = element_blank()) + 
  # theme(axis.title.x = element_blank(),
  #       axis.title.y = element_blank(),
  #       axis.text.y = element_blank(),  # Remove y-axis text
  #       axis.ticks.y = element_blank()) +  # Remove y-axis ticks 
  ggtitle("Females")

# Combine the two plots into one panel using cowplot
combined_plot_sma <- plot_grid(p1_males_sma, p2_females_sma, ncol = 2)

# Display the combined plot
print(combined_plot_sma)


# Save the plots
ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.ColocGrouped.SMAonly.GenderMales.png"), plot = p1_males_sma)
ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.ColocGrouped.SMAonly.GenderMales.ps"), plot = p1_males_sma)
ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.ColocGrouped.SMAonly.GenderMales.pdf"), plot = p1_males_sma)

ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.ColocGrouped.SMAonly.GenderFemales.png"), plot = p2_females_sma)
ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.ColocGrouped.SMAonly.GenderFemales.ps"), plot = p2_females_sma)
ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.ColocGrouped.SMAonly.GenderFemales.pdf"), plot = p2_females_sma)

ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.ColocGrouped.SMAonly.Gender.png"), plot = combined_plot_sma)
ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.ColocGrouped.SMAonly.Gender.ps"), plot = combined_plot_sma)
ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.ColocGrouped.SMAonly.Gender.pdf"), plot = combined_plot_sma)

```

```{r Visualisation: Targets Feature Plots, message=FALSE, warning=FALSE}
FeaturePlot(scRNAseqDataCEA39, features = c(target_genes_qc),
            cols =  c("#ECECEC", "#DB003F", "#9A3480","#1290D9"),
            combine = TRUE)

ggsave(paste0(PLOT_loc, "/", Today, ".FeaturePlot.Targets.png"), plot = last_plot())
ggsave(paste0(PLOT_loc, "/", Today, ".FeaturePlot.Targets.ps"), plot = last_plot())

```

```{r Visualisation: Targets}
# VlnPlot(scRNAseqDataCEA39, features = "DUSP27")

# VlnPlot files
ifelse(!dir.exists(file.path(PLOT_loc, "/VlnPlot")), 
       dir.create(file.path(PLOT_loc, "/VlnPlot")), 
       FALSE)
VlnPlot_loc = paste0(PLOT_loc, "/VlnPlot")


for (GENE in target_genes_qc){
  print(paste0("Projecting the expression of ", GENE, "."))

  vp1 <-  VlnPlot(scRNAseqDataCEA39, features = GENE) + 
    xlab("cell communities") + 
    ylab(bquote("normalized expression")) +
    theme(axis.title.x = element_text(color = "#000000", size = 14, face = "bold"), 
            axis.title.y = element_text(color = "#000000", size = 14, face = "bold"), 
            legend.position = "none")
    ggsave(paste0(VlnPlot_loc, "/", Today, ".VlnPlot.",GENE,".png"), plot = last_plot())
    ggsave(paste0(VlnPlot_loc, "/", Today, ".VlnPlot.",GENE,".ps"), plot = last_plot())
    ggsave(paste0(VlnPlot_loc, "/", Today, ".VlnPlot.",GENE,".pdf"), plot = last_plot())
  
  # print(vp1)
  
}

```

### Differential expression between cell communities

Here we project genes to only the broad cell communities:

-   macrophages
-   endothelial cells
-   smooth muscle cells
-   T-cells
-   B-cells
-   Mast cells
-   NK-cells
-   Mixed cells

#### Macrophages

```{r}
unique(scRNAseqDataCEA39@active.ident)
```

Comparison between the macrophages cell communities (*CD14/CD68*<sup>+</sup>),
and all other communities.

```{r Visualisation: Volcano MAC calculate}

MAC.markers <- FindMarkers(object = scRNAseqDataCEA39, 
                          ident.1 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC"), 
                          ident.2 = c(#"CD68+CASP1+IL1B+SELL MInf", 
                                      #"CD68+CD1C+ DC", 
                                      #"CD68+CD4+ Mono",
                                      #"CD68+IL18+TLR4+TREM2+ MRes",
                                      #"CD68+ABCA1+OLR1+TREM2+ FC",
                                      "CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "ACTA2+ SMC", 
                                      "CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II", 
                                      "CD68+KIT+ MC",
                                      "CD79+ BCplasma", 
                                      "CD79A+ BCmem"))

DT::datatable(MAC.markers)
```

```{r Visualisation: Volcano MAC, message=FALSE, warning=FALSE}
MAC_Volcano_TargetsA = EnhancedVolcano(MAC.markers,
    lab = rownames(MAC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "Macrophage markers\n(Macrophage communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/(nrow(MAC.markers)), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
MAC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.MAC.DEG.Targets.pdf"), 
       plot = MAC_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results MAC}
library(tibble)
MAC.markers <- add_column(MAC.markers, Gene = row.names(MAC.markers), .before = 1)

temp <- MAC.markers[MAC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results MAC: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".MAC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

#### Smooth muscle cells

Comparison between the smooth muscle cell communities (*ACTA2*<sup>+</sup>), and
all other communities.

```{r Visualisation: Volcano SMC calculate}

SMC.markers <- FindMarkers(object = scRNAseqDataCEA39, 
                          ident.1 = c("ACTA2+ SMC"), 
                          ident.2 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC",
                                      "CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      #"ACTA2+ SMC", 
                                      "CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II", 
                                      "CD68+KIT+ MC",
                                      "CD79+ BCplasma", 
                                      "CD79A+ BCmem"))

DT::datatable(SMC.markers)
```

```{r Visualisation: Volcano SMC, message=FALSE, warning=FALSE}
SMC_Volcano_TargetsA = EnhancedVolcano(SMC.markers,
    lab = rownames(SMC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "SMC markers\n(SMC communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/(nrow(SMC.markers)), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
SMC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.SMC.DEG.Targets.pdf"), 
       plot = SMC_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results SMC}
library(tibble)
SMC.markers <- add_column(SMC.markers, Gene = row.names(SMC.markers), .before = 1)

temp <- SMC.markers[SMC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results SMC: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".SMC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

#### Endothelial cells

Comparison between the endothelial cell communities (*CD34*<sup>+</sup>), and
all other communities.

```{r Visualisation: Volcano EC calculate}

EC.markers <- FindMarkers(object = scRNAseqDataCEA39, 
                          ident.1 = c("CD34+ EC I", 
                                      "CD34+ EC II"), 
                          ident.2 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC",
                                      "CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC", 
                                      # "CD34+ EC I", 
                                      # "CD34+ EC II",
                                      "ACTA2+ SMC", 
                                      "CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II", 
                                      "CD68+KIT+ MC",
                                      "CD79+ BCplasma", 
                                      "CD79A+ BCmem"))

DT::datatable(EC.markers)
```

```{r Visualisation: Volcano EC, message=FALSE, warning=FALSE}
EC_Volcano_TargetsA = EnhancedVolcano(EC.markers,
    lab = rownames(EC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "Endothelial cell markers\n(EC communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/(nrow(EC.markers)), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
EC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.EC.DEG.Targets.pdf"), 
       plot = EC_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results EC}
library(tibble)
EC.markers <- add_column(EC.markers, Gene = row.names(EC.markers), .before = 1)

temp <- EC.markers[EC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results EC: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".EC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

#### T-cells

Comparison between the T-cell communities (*CD3/CD4/CD8*<sup>+</sup>), and all
other communities.

```{r Visualisation: Volcano Tcell calculate}

TC.markers <- FindMarkers(object = scRNAseqDataCEA39, 
                          ident.1 = c("CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC"), 
                          ident.2 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC",
                                      # "CD3+ TC I",
                                      # "CD3+ TC II", 
                                      # "CD3+ TC III", 
                                      # "CD3+ TC IV", 
                                      # "CD3+ TC V", 
                                      # "CD3+ TC VI", 
                                      # "FOXP3+ TC", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "ACTA2+ SMC", 
                                      "CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II", 
                                      "CD68+KIT+ MC",
                                      "CD79+ BCplasma", 
                                      "CD79A+ BCmem"))

DT::datatable(TC.markers)
```

```{r Visualisation: Volcano Tcell, message=FALSE, warning=FALSE}
TC_Volcano_TargetsA = EnhancedVolcano(TC.markers,
    lab = rownames(TC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "T-cell markers\n(T-cell communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/nrow(TC.markers), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
TC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.TC.DEG.Targets.pdf"), 
       plot = TC_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results TC}
library(tibble)
TC.markers <- add_column(TC.markers, Gene = row.names(TC.markers), .before = 1)

temp <- TC.markers[TC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results TC: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".TC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

#### B-cells

Comparison between the B-cell communities (*CD79A*<sup>+</sup>), and all other
communities.

```{r Visualisation: Volcano Bcell calculate}

BC.markers <- FindMarkers(object = scRNAseqDataCEA39, 
                          ident.1 = c("CD79+ BCplasma", 
                                      "CD79A+ BCmem"), 
                          ident.2 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC",
                                      "CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "ACTA2+ SMC", 
                                      "CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II", 
                                      "CD68+KIT+ MC"
                                      # "CD79+ BCplasma", 
                                      # "CD79A+ BCmem"
                                      ))

DT::datatable(BC.markers)
```

```{r Visualisation: Volcano Bcell, message=FALSE, warning=FALSE}
BC_Volcano_TargetsA = EnhancedVolcano(BC.markers,
    lab = rownames(BC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "B-cell markers\n(B-cell communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/nrow(BC.markers), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
BC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.BC.DEG.Targets.pdf"), 
       plot = BC_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results BC}
library(tibble)
BC.markers <- add_column(BC.markers, Gene = row.names(BC.markers), .before = 1)

temp <- BC.markers[BC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results BC: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".BC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

#### Mast cells

Comparison between the mast cell communities (*KIT*<sup>+</sup>), and all other
communities.

```{r Visualisation: Volcano Mast calculate}

MC.markers <- FindMarkers(object = scRNAseqDataCEA39, 
                          ident.1 = c("CD68+KIT+ MC"), 
                          ident.2 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC",
                                      "CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "ACTA2+ SMC", 
                                      "CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II", 
                                      # "CD68+KIT+ MC",
                                      "CD79+ BCplasma", 
                                      "CD79A+ BCmem"))

DT::datatable(MC.markers)
```

```{r Visualisation: Volcano Mast, message=FALSE, warning=FALSE}
MC_Volcano_TargetsA = EnhancedVolcano(MC.markers,
    lab = rownames(MC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "Mast cell markers\n(Mast cell communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/nrow(MC.markers), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
MC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.MC.DEG.Targets.pdf"), 
       plot = MC_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results MC}
library(tibble)
MC.markers <- add_column(MC.markers, Gene = row.names(MC.markers), .before = 1)

temp <- MC.markers[MC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results MC: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".MC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

#### NK-cells

Comparison between the natural killer cell communities (*NCAM1*<sup>+</sup>),
and all other communities.

```{r Visualisation: Volcano NK calculate}

NK.markers <- FindMarkers(object = scRNAseqDataCEA39, 
                          ident.1 = c("CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II"), 
                          ident.2 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC",
                                      "CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "ACTA2+ SMC", 
                                      # "CD3+CD56+ NK I",
                                      # "CD3+CD56+ NK II", 
                                      "CD68+KIT+ MC",
                                      "CD79+ BCplasma", 
                                      "CD79A+ BCmem"))

DT::datatable(NK.markers)
```

```{r Visualisation: Volcano NK, message=FALSE, warning=FALSE}
NK_Volcano_TargetsA = EnhancedVolcano(NK.markers,
    lab = rownames(NK.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "NK markers\n(NK-cell communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/nrow(NK.markers), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
NK_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.NK.DEG.Targets.pdf"), 
       plot = NK_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results NK}
library(tibble)
NK.markers <- add_column(NK.markers, Gene = row.names(NK.markers), .before = 1)

temp <- NK.markers[NK.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results NK: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".NK.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```



# Session information

--------------------------------------------------------------------------------

    Version:      v1.2.0
    Last update:  2024-10-17
    Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
    Description:  Script to load single-cell RNA sequencing (scRNAseq) data, and perform quality control (QC), and initial mapping to cells.
    Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).

    **MoSCoW To-Do List**
    The things we Must, Should, Could, and Would have given the time we have.
    _M_

    _S_

    _C_

    _W_

    **Changes log**
    * v1.2.0 Fixed an issue where the subset of scRNAseq was loosing cell-identities.
    * v1.1.1 Textual fixes.
    * v1.1.1 Fix writing baseline table.
    * v1.1.0 Update to study database.
    * v1.0.2 Fixes to the start of the notebook. Update to loading of the clinical data. Fix on the gene-filtering.
    * v1.0.1 Update to main AEDB (there is an error in the Age-variable in the new version). Fewer patients in scRNAseq (32 vs 39 with the newer dataset).
    * v1.0.0 Initial version.

--------------------------------------------------------------------------------

```{r eval = TRUE}
sessionInfo()
```

# Saving environment

```{r Saving}
rm(backup.scRNAseqData)
rm(scRNAseqData, scRNAseqDataCEA39)

save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".AESCRNA.results.RData"))

```

+-----------------------------------------------------------------------------------------------------------------------------------------+
| <sup>© 1979-2024 Sander W. van der Laan | s.w.vanderlaan[at]gmail[dot]com | [vanderlaanand.science](https://vanderlaanand.science).</sup> |
+-----------------------------------------------------------------------------------------------------------------------------------------+

